# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Fashion-MNIST dataset.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import gzip
import os

import numpy as np

from keras.utils.data_utils import get_file
from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.datasets.fashion_mnist.load_data')
def load_data():
  """Loads the Fashion-MNIST dataset.

  This is a dataset of 60,000 28x28 grayscale images of 10 fashion categories,
  along with a test set of 10,000 images. This dataset can be used as
  a drop-in replacement for MNIST.

  The classes are:

  | Label | Description |
  |:-----:|-------------|
  |   0   | T-shirt/top |
  |   1   | Trouser     |
  |   2   | Pullover    |
  |   3   | Dress       |
  |   4   | Coat        |
  |   5   | Sandal      |
  |   6   | Shirt       |
  |   7   | Sneaker     |
  |   8   | Bag         |
  |   9   | Ankle boot  |

  Returns:
    Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`.

  **x_train**: uint8 NumPy array of grayscale image data with shapes
    `(60000, 28, 28)`, containing the training data.

  **y_train**: uint8 NumPy array of labels (integers in range 0-9)
    with shape `(60000,)` for the training data.

  **x_test**: uint8 NumPy array of grayscale image data with shapes
    (10000, 28, 28), containing the test data.

  **y_test**: uint8 NumPy array of labels (integers in range 0-9)
    with shape `(10000,)` for the test data.

  Example:

  ```python
  (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
  assert x_train.shape == (60000, 28, 28)
  assert x_test.shape == (10000, 28, 28)
  assert y_train.shape == (60000,)
  assert y_test.shape == (10000,)
  ```

  License:
    The copyright for Fashion-MNIST is held by Zalando SE.
    Fashion-MNIST is licensed under the [MIT license](
    https://github.com/zalandoresearch/fashion-mnist/blob/master/LICENSE).

  """
  dirname = os.path.join('datasets', 'fashion-mnist')
  base = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
  files = [
      'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',
      't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'
  ]

  paths = []
  for fname in files:
    paths.append(get_file(fname, origin=base + fname, cache_subdir=dirname))

  with gzip.open(paths[0], 'rb') as lbpath:
    y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)

  with gzip.open(paths[1], 'rb') as imgpath:
    x_train = np.frombuffer(
        imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)

  with gzip.open(paths[2], 'rb') as lbpath:
    y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)

  with gzip.open(paths[3], 'rb') as imgpath:
    x_test = np.frombuffer(
        imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)

  return (x_train, y_train), (x_test, y_test)
